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1. Identifying topologically associating domains using differential kernels

   https://doi.org/10.1371/journal.pcbi.1012221  

   Maisuradze, Luka; King, Megan C; Surovtsev, Ivan V; Mochrie, Simon_G J; Shattuck, Mark D; O’Hern, Corey S (July 2024, PLOS Computational Biology)

   Dunbrack, Roland L (Ed.)

   Chromatin is a polymer complex of DNA and proteins that regulates gene expression. The three-dimensional (3D) structure and organization of chromatin controls DNA transcription and replication. High-throughput chromatin conformation capture techniques generate Hi-C maps that can provide insight into the 3D structure of chromatin. Hi-C maps can be represented as a symmetric matrix ${\mathcal{A}}_{ij}$, where each element represents the average contact probability or number of contacts between chromatin lociiandj. Previous studies have detected topologically associating domains (TADs), or self-interacting regions in ${\mathcal{A}}_{ij}$within which the contact probability is greater than that outside the region. Many algorithms have been developed to identify TADs within Hi-C maps. However, most TAD identification algorithms are unable to identify nested or overlapping TADs and for a given Hi-C map there is significant variation in the location and number of TADs identified by different methods. We develop a novel method to identify TADs, KerTAD, using a kernel-based technique from computer vision and image processing that is able to accurately identify nested and overlapping TADs. We benchmark this method against state-of-the-art TAD identification methods on both synthetic and experimental data sets. We find that the new method consistently has higher true positive rates (TPR) and lower false discovery rates (FDR) than all tested methods for both synthetic and manually annotated experimental Hi-C maps. The TPR for KerTAD is also largely insensitive to increasing noise and sparsity, in contrast to the other methods. We also find that KerTAD is consistent in the number and size of TADs identified across replicate experimental Hi-C maps for several organisms. Thus, KerTAD will improve automated TAD identification and enable researchers to better correlate changes in TADs to biological phenomena, such as enhancer-promoter interactions and disease states. 

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2. DiffGR: Detecting Differentially Interacting Genomic Regions from Hi-C Contact Maps

   https://doi.org/10.1093/gpbjnl/qzae028  

   Liu, Huiling; Ma, Wenxiu (March 2024, Genomics, Proteomics & Bioinformatics)

   Zhang, Zhihua (Ed.)

   Recent advances in high-throughput chromosome conformation capture (Hi-C) techniques have allowed us to map genome-wide chromatin interactions and uncover higher-order chromatin structures, thereby shedding light on the principles of genome architecture and functions. However, statistical methods for detecting changes in large-scale chromatin organization such as topologically associating domains (TADs) are still lacking. Here, we proposed a new statistical method, DiffGR, for detecting differentially interacting genomic regions at the TAD level between Hi-C contact maps. We utilized the stratum-adjusted correlation coefficient to measure similarity of local TAD regions. We then developed a nonparametric approach to identify statistically significant changes of genomic interacting regions. Through simulation studies, we demonstrated that DiffGR can robustly and effectively discover differential genomic regions under various conditions. Furthermore, we successfully revealed cell type-specific changes in genomic interacting regions in both human and mouse Hi-C datasets, and illustrated that DiffGR yielded consistent and advantageous results compared with state-of-the-art differential TAD detection methods. The DiffGR R package is published under the GNU General Public License (GPL) ≥ 2 license and is publicly available at https://github.com/wmalab/DiffGR. 

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3. A Heuristic Strategy for Multi-Mapping Reads to Enhance Hi-C Data

   https://doi.org/10.1109/BIBE52308.2021.9635215  

   Bulathsinghalage, Chanaka; Liu, Lu (October 2021, 2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE))

   Current Hi-C analysis approaches focus on uniquely mapped reads and little research has been carried out to include multi-mapping reads, which leads to a lack of biological signals from DNA repetitive regions. We propose a heuristic strategy to assign multi-mapping reads to loci according to the distance to their closest restriction enzyme cutting sites. We demonstrate that the heuristic strategy can rescue multi-mapping reads thus enhance the quality of Hi-C data. Compared with mHi-C, it not only improves replicate reproducibility in the same cell type, but also maintains the difference between replicates of different cell types. Moreover, the strategy identifies much more common statistically significant chromatin interactions between Hi-C experiments of different restriction enzymes and has a huge advantage on computing resources. Therefore, the heuristic strategy can be used to enhance Hi-C data by utilizing multi-mapping reads. 

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4. ASHIC: hierarchical Bayesian modeling of diploid chromatin contacts and structures

   https://doi.org/10.1093/nar/gkaa872  

   Ye, Tiantian; Ma, Wenxiu (October 2020, Nucleic Acids Research)

   null (Ed.)

   Abstract The recently developed Hi-C technique has been widely applied to map genome-wide chromatin interactions. However, current methods for analyzing diploid Hi-C data cannot fully distinguish between homologous chromosomes. Consequently, the existing diploid Hi-C analyses are based on sparse and inaccurate allele-specific contact matrices, which might lead to incorrect modeling of diploid genome architecture. Here we present ASHIC, a hierarchical Bayesian framework to model allele-specific chromatin organizations in diploid genomes. We developed two models under the Bayesian framework: the Poisson-multinomial (ASHIC-PM) model and the zero-inflated Poisson-multinomial (ASHIC-ZIPM) model. The proposed ASHIC methods impute allele-specific contact maps from diploid Hi-C data and simultaneously infer allelic 3D structures. Through simulation studies, we demonstrated that ASHIC methods outperformed existing approaches, especially under low coverage and low SNP density conditions. Additionally, in the analyses of diploid Hi-C datasets in mouse and human, our ASHIC-ZIPM method produced fine-resolution diploid chromatin maps and 3D structures and provided insights into the allelic chromatin organizations and functions. To summarize, our work provides a statistically rigorous framework for investigating fine-scale allele-specific chromatin conformations. The ASHIC software is publicly available at https://github.com/wmalab/ASHIC. 

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5. scHiCDiff: detecting differential chromatin interactions in single-cell Hi-C data

   https://doi.org/10.1093/bioinformatics/btad625  

   Liu, Huiling; Ma, Wenxiu; Marschall, ed., Tobias (October 2023, Bioinformatics)

   Abstract SummaryHere, we presented the scHiCDiff software tool that provides both nonparametric tests and parametirc models to detect differential chromatin interactions (DCIs) from single-cell Hi-C data. We thoroughly evaluated the scHiCDiff methods on both simulated and real data. Our results demonstrated that scHiCDiff, especially the zero-inflated negative binomial model option, can effectively detect reliable and consistent single-cell DCIs between two conditions, thereby facilitating the study of cell type-specific variations of chromatin structures at the single-cell level. Availability and implementationscHiCDiff is implemented in R and freely available at GitHub (https://github.com/wmalab/scHiCDiff). 

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